Senior Machine Learning Engineer

ABOUT THE ROLE We're seeking a highly skilled Applied AI Engineer to join our team at ZoomInfo, where careers accelerate. As a member of our Applied AI team, you'll be part of a collaborative and dynamic environment that empowers you to do the best work of your life. You'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. The Applied AI team builds the intelligence layer that sits between ZoomInfo's high-quality data and the application and agentic layer through which customers engage. Using a product-led growth model, this team leverages customer engagement as input to build better recommendations, scoring, classification, and generative models. WHAT YOU'LL DO As an Applied AI Engineer, you'll be responsible for designing and developing large-scale recommendation systems, advanced NLP and embedding systems, MLOps lifecycle management, agentic workflows and evaluation, and cross-functional collaboration. Your key responsibilities will include: - Building large-scale recommendation systems utilizing embeddings generated for structured and unstructured data using methods such as a two-tower architecture - Performing recommendation designs that can scale to millions of recommendations per day for different product features - Utilizing graph-based structures, search, and scoring to enhance recommendation quality - Fine-tuning, customizing, and deploying embedding models for multi-language text understanding and semantic search - Architecting vector search solutions that enable language-agnostic clustering and classification across global datasets - Building and optimizing high-performance retrieval systems using vector databases - Architecting and managing scalable MLOps and LLMOps infrastructure for robust model training, evaluation, deployment, and monitoring systems - Designing comprehensive CI/CD pipelines, implementing model monitoring frameworks to identify drift patterns, and ensuring high availability and fault tolerance - Helping establish metrics, experimentation frameworks, and statistical validation approaches for AI system performance - Designing and implementing agentic systems for automated web extraction, NER, and entity resolution tasks - Building comprehensive evaluation frameworks for agent performance across data acquisition and processing workflows - Creating feedback loops that continuously improve agent decision-making and data quality outcomes - Building, and scaling MCP servers and integrating them into broader AI and product ecosystems - Collaborating with engineering teams to ensure models integrate seamlessly and scale with business needs - Working with Product Management to translate business requirements into scalable ML solutions WHAT YOU'LL NEED To be successful in this role, you'll need: - 6+ years hands-on ML/NLP experience (or 3+ years post-PhD/Master's) with at least two delivered, revenue-impacting products in production environments - Expertise in modern AI architectures including transformer stacks, prompt engineering, RAG systems, vector-based information retrieval, and context engineering - Proven track record building and managing production systems by architecting and deploying scalable distributed systems of REST & MCP based microservices for applications and agents with observability and monitoring of latency, token utilization, and system reliability - Strong applied research capabilities (PyTorch or TensorFlow) paired with software-engineering rigor (Python) and familiarity with open weight LLMs (QWEN, Gemma, OSS) and embedding models and vector search technologies (FAISS, Pinecone) - Executive